{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6be49461",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import datetime as timedelta\n",
    "import matplotlib.mlab as ml\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e315a069",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def concat(*arrs) -> np.ndarray:\n",
    "    return np.concatenate(tuple(map(np.asarray, arrs)))\n",
    "\n",
    "def insert_at(outer_arr, arr, n) -> np.ndarray:\n",
    "    outer_arr = np.asarray(outer_arr)\n",
    "    prev, post = np.split(outer_arr, (n,))\n",
    "    return concat(prev, arr, post)\n",
    "\n",
    "def cross2d(x1, y1, x2, y2):\n",
    "    return x1*y2-x2*y1\n",
    "\n",
    "def is_clockwise(x1, y1, x2, y2):\n",
    "    cp = cross2d(x1, y1, x2, y2)\n",
    "    return cp < 0 if cp != 0 else None\n",
    "\n",
    "def fill_outside(x, y, ll, ur, counter_clockwise=None):\n",
    "    \"\"\"\n",
    "    Creates a polygon where x and y form a crevice of an outer\n",
    "    rectangle with lower left and upper right corners `ll` and `ur`\n",
    "    respectively. If `counter_clockwise` is `None` then the orientation\n",
    "    of the outer polygon will be guessed to be the opposite of the\n",
    "    inner connecting points.\n",
    "    \"\"\"\n",
    "    x = np.asarray(x)\n",
    "    y = np.asarray(y)\n",
    "    xmin, ymin = ll\n",
    "    xmax, ymax = ur\n",
    "    xmin, ymin = min(xmin, min(x)), min(ymin, min(y))\n",
    "    xmax, ymax = max(xmax, max(x)), max(ymax, max(y))\n",
    "    corners = np.array([\n",
    "        [xmin, ymin],\n",
    "        [xmin, ymax],\n",
    "        [xmax, ymax],\n",
    "        [xmax, ymin],\n",
    "        [xmin, ymin],\n",
    "    ])\n",
    "    lower_left = corners[0]\n",
    "    # Get closest point to splicing corner\n",
    "    x_off, y_off = x-lower_left[0], y-lower_left[1]\n",
    "    closest_n = (x_off**2+y_off**2).argmin()\n",
    "    # Guess orientation\n",
    "    p = [x_off[closest_n], y_off[closest_n]]\n",
    "    try:\n",
    "        pn = [x_off[closest_n+1], y_off[closest_n+1]]\n",
    "    except IndexError:\n",
    "        # wrap around if we're at the end of the array\n",
    "        pn = [x_off[0], y_off[0]]\n",
    "    if counter_clockwise is None:\n",
    "        counter_clockwise = not is_clockwise(*p, *pn)\n",
    "    corners = corners[::-1] if counter_clockwise else corners\n",
    "    # Join the arrays\n",
    "    corners = concat(np.array([[x[closest_n], y[closest_n]]]), corners)\n",
    "    xs, ys = np.transpose(corners)\n",
    "    return insert_at(x, xs, closest_n), insert_at(y, ys, closest_n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e638f043",
   "metadata": {},
   "outputs": [],
   "source": [
    "DJR= pd.read_csv('DJR.csv')\n",
    "\n",
    "df1 = pd.read_csv('ID1.csv')\n",
    "df2 = pd.read_csv('ID2.csv')\n",
    "df3 = pd.read_csv('ID3.csv')\n",
    "df4 = pd.read_csv('ID4.csv')\n",
    "df5 = pd.read_csv('ID5.csv')\n",
    "df6 = pd.read_csv('ID6.csv')\n",
    "df7 = pd.read_csv('ID7.csv')\n",
    "df8 = pd.read_csv('ID8.csv')\n",
    "df9 = pd.read_csv('ID9.csv')\n",
    "df10 = pd.read_csv('ID10.csv')\n",
    "df11 = pd.read_csv('ID11.csv')\n",
    "df12 = pd.read_csv('ID12.csv')\n",
    "df13 = pd.read_csv('ID13.csv')\n",
    "df14 = pd.read_csv('ID14.csv')\n",
    "df15 = pd.read_csv('ID15.csv')\n",
    "df16 = pd.read_csv('ID16.csv')\n",
    "df17 = pd.read_csv('ID17.csv')\n",
    "df18 = pd.read_csv('ID18.csv')\n",
    "df19 = pd.read_csv('ID19.csv')\n",
    "df20 = pd.read_csv('ID20.csv')\n",
    "df21 = pd.read_csv('ID21.csv')\n",
    "df22 = pd.read_csv('ID22.csv')\n",
    "df23 = pd.read_csv('ID23.csv')\n",
    "df24 = pd.read_csv('ID24.csv')\n",
    "df25 = pd.read_csv('ID25.csv')\n",
    "df26 = pd.read_csv('ID26.csv')\n",
    "df27 = pd.read_csv('ID27.csv')\n",
    "df28 = pd.read_csv('ID28.csv')\n",
    "df29 = pd.read_csv('ID29.csv')\n",
    "df30 = pd.read_csv('ID30.csv')\n",
    "df31 = pd.read_csv('ID31.csv')\n",
    "df32 = pd.read_csv('ID32.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "35bd5e5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-5-57d2406e61fe>:464: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  plt.subplot(8,4,20)\n",
      "<ipython-input-5-57d2406e61fe>:487: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  plt.subplot(8,4,21)\n",
      "<ipython-input-5-57d2406e61fe>:509: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  plt.subplot(8,4,22)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 388.8x748.8 with 32 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5.4,10.4))\n",
    "\n",
    "ll, ur = (129.291, 36.026), (129.2955, 36.032)\n",
    "plt.rc('font', size = 10)\n",
    "plt.rc('axes', labelsize = 10)\n",
    "plt.rc('xtick', labelsize = 10)\n",
    "plt.rc('ytick', labelsize = 10)\n",
    "plt.rc('legend', fontsize = 10)\n",
    "plt.rc('figure', titlesize = 10)\n",
    "\n",
    "\n",
    "plt.subplot(8,4,1)\n",
    "\n",
    "x, y = fill_outside(DJR[\"LON\"], DJR[\"LAT\"], ll, ur)\n",
    "\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df1[\"LON\"], df1[\"LAT\"], s= 0.1, color = 'black')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "\n",
    "plt.subplot(8,4,2)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df2[\"LON\"], df2[\"LAT\"], s= 0.1, color = 'black')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "\n",
    "plt.subplot(8,4,3)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df3[\"LON\"], df3[\"LAT\"], s= 0.1, color = 'black')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "\n",
    "plt.subplot(8,4,4)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df4[\"LON\"], df4[\"LAT\"], s= 0.1, color = 'black')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "plt.subplot(8,4,5)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df5[\"LON\"], df5[\"LAT\"], s= 0.1, color = 'black')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "plt.subplot(8,4,6)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df6[\"LON\"], df6[\"LAT\"], s= 0.1, color = 'black')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "plt.subplot(8,4,7)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df7[\"LON\"], df7[\"LAT\"], s= 0.1, color = 'black')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "plt.subplot(8,4,8)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df8[\"LON\"], df8[\"LAT\"], s= 0.1, color = 'black')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "\n",
    "plt.subplot(8,4,9)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df9[\"LON\"], df9[\"LAT\"], s= 0.1, color = 'black')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "\n",
    "plt.subplot(8,4,10)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df10[\"LON\"], df10[\"LAT\"], s= 0.1, color = 'black')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "\n",
    "\n",
    "plt.subplot(8,4,11)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df11[\"LON\"], df11[\"LAT\"], s= 0.1, color = 'black')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "plt.subplot(8,4,12)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df12[\"LON\"], df12[\"LAT\"], s= 0.1, color = 'black')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "plt.subplot(8,4,13)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df13[\"LON\"], df13[\"LAT\"], s= 0.1, color = 'black')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "plt.subplot(8,4,14)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df14[\"LON\"], df14[\"LAT\"], s= 0.1, color = 'black')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "plt.subplot(8,4,15)\n",
    "\n",
    "#for axis in ['top', 'bottom', 'left', 'right']:\n",
    "#  plt.gca().spines[axis].set_linewidth(3)\n",
    "\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df15[\"LON\"], df15[\"LAT\"], s= 0.1, color = 'black')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "\n",
    "plt.subplot(8,4,16)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df16[\"LON\"], df16[\"LAT\"], s= 0.1, color = 'black')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "plt.subplot(8,4,17)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df17[\"LON\"], df17[\"LAT\"], s= 0.1, color = 'black')\n",
    "\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "plt.subplot(8,4,18)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df18[\"LON\"], df18[\"LAT\"], s= 0.1, color = 'black')\n",
    "\n",
    "\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "plt.subplot(8,4,19)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df19[\"LON\"], df19[\"LAT\"], s= 0.1, color = 'black')\n",
    "\n",
    "\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "plt.grid()\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "############################\n",
    "##############################\n",
    "############################\n",
    "plt.subplot(8,4,20)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df20[\"LON\"], df20[\"LAT\"], s= 0.1, color = 'black')\n",
    "\n",
    "\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "plt.grid()\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "############################\n",
    "##############################\n",
    "############################\n",
    "plt.subplot(8,4,21)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df21[\"LON\"], df21[\"LAT\"], s= 0.1, color = 'black')\n",
    "\n",
    "\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "plt.grid()\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "############################\n",
    "##############################\n",
    "############################\n",
    "plt.subplot(8,4,22)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df22[\"LON\"], df22[\"LAT\"], s= 0.1, color = 'black')\n",
    "\n",
    "\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "plt.grid()\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "############################\n",
    "##############################\n",
    "############################\n",
    "plt.subplot(8,4,20)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df20[\"LON\"], df20[\"LAT\"], s= 0.1, color = 'black')\n",
    "\n",
    "\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "plt.grid()\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "############################\n",
    "##############################\n",
    "############################\n",
    "plt.subplot(8,4,21)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df21[\"LON\"], df21[\"LAT\"], s= 0.1, color = 'black')\n",
    "\n",
    "\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "plt.grid()\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "############################\n",
    "##############################\n",
    "############################\n",
    "plt.subplot(8,4,22)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df22[\"LON\"], df22[\"LAT\"], s= 0.1, color = 'black')\n",
    "\n",
    "\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "plt.grid()\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "############################\n",
    "##############################\n",
    "############################\n",
    "plt.subplot(8,4,23)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df23[\"LON\"], df23[\"LAT\"], s= 0.1, color = 'black')\n",
    "\n",
    "\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "plt.grid()\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "############################\n",
    "##############################\n",
    "############################\n",
    "plt.subplot(8,4,24)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df24[\"LON\"], df24[\"LAT\"], s= 0.1, color = 'black')\n",
    "\n",
    "\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "plt.grid()\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "############################\n",
    "##############################\n",
    "############################\n",
    "plt.subplot(8,4,25)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df25[\"LON\"], df25[\"LAT\"], s= 0.1, color = 'black')\n",
    "\n",
    "\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "plt.grid()\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "############################\n",
    "##############################\n",
    "############################\n",
    "plt.subplot(8,4,26)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df26[\"LON\"], df26[\"LAT\"], s= 0.1, color = 'black')\n",
    "\n",
    "\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "plt.grid()\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "############################\n",
    "##############################\n",
    "############################\n",
    "plt.subplot(8,4,27)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df27[\"LON\"], df27[\"LAT\"], s= 0.1, color = 'black')\n",
    "\n",
    "\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "plt.grid()\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "############################\n",
    "##############################\n",
    "############################\n",
    "plt.subplot(8,4,28)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df28[\"LON\"], df28[\"LAT\"], s= 0.1, color = 'black')\n",
    "\n",
    "\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "plt.grid()\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "############################\n",
    "##############################\n",
    "############################\n",
    "plt.subplot(8,4,29)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df29[\"LON\"], df29[\"LAT\"], s= 0.1, color = 'black')\n",
    "\n",
    "\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "plt.grid()\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "############################\n",
    "##############################\n",
    "############################\n",
    "plt.subplot(8,4,30)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df30[\"LON\"], df30[\"LAT\"], s= 0.1, color = 'black')\n",
    "\n",
    "\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "plt.grid()\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "############################\n",
    "##############################\n",
    "############################\n",
    "plt.subplot(8,4,31)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df31[\"LON\"], df31[\"LAT\"], s= 0.1, color = 'black')\n",
    "\n",
    "\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "plt.grid()\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "############################\n",
    "##############################\n",
    "############################\n",
    "plt.subplot(8,4,32)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df32[\"LON\"], df32[\"LAT\"], s= 0.1, color = 'black')\n",
    "\n",
    "\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "plt.grid()\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "plt.savefig('Figure 2.png',bbox_inches = \"tight\", dpi = 600)"
   ]
  }
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